Conference Agenda

The Soil Moisture and Ocean Salinity (SMOS) mission has played a key role in monitoring global soil moisture, providing valuable measurements over the last 15 years. This research has focused on improving SMOS products by refining the algorithms, integrating data from multiple sensors, and addressing spatial and temporal gaps in soil moisture data. While SMOS, equipped with an L-band microwave radiometer, has delivered important insights, there have been ongoing challenges in improving the accuracy and consistency of SMOS soil moisture product, especially when comparing data from SMOS with other missions like SMAP. One major achievement of this research has been the development of techniques to reduce the discrepancies between SMOS and SMAP products. By correcting systematic biases in the brightness temperature (TB) measurements, particularly those arising from differences in observation angles and atmospheric effects, the research has helped improve data consistency. Additionally, the research has explored the use of multi-angular data from SMOS to enhance soil moisture retrieval through a Multi Channel Collaborative Algorithm (MCCA). This approach has effectively utilized multi-angular brightness temperature data and vegetation optical depth to improve the accuracy of soil moisture retrieval, especially in regions with varying surface characteristics. Another important outcome has been the enhancement of spatial resolution. The research combined passive and active microwave measurements with optical and infrared data to downscale SMOS and SMAP soil moisture products to finer spatial resolutions, such as 1 km. The incorporation of machine learning techniques has further improved the temporal resolution of these products, enabling the creation of long-term, high-resolution datasets spanning several decades. In addition, this research has utilized in-situ data from regions such as the Tibetan Plateau, which has been crucial in validating satellite-derived soil moisture products. These data have improved our understanding of soil moisture dynamics, particularly in challenging environments like permafrost and seasonally frozen ground. By combining in-situ data with satellite observations, this research has provided better insights into drought monitoring and agricultural forecasting, supporting more accurate assessments of global water cycles and climate variability. The work has led to the development of a harmonized soil moisture product with improved spatial and temporal resolution, filling important gaps in global change research. By improving the consistency of SMOS and SMAP products, the research has laid the groundwork for a more reliable, long-term soil moisture monitoring system, which will be valuable for research in hydrology, agriculture, and climate science.

Glaciers in High Mountain Asia (HMA) are important water sources for millions of people in arid regions. Mass balance states of the glaciers exhibit significant differences in different subregions. However, the reasons for this have been unclear due to limited accurate temperature and precipitation datasets in extremely high altitudes, bringing great uncertainty to the survival and development of the people in different subregions. In this work, the ablation index and seasonal accumulation index are proposed based on firn and snow state observation from satellite, which present the ablation and accumulation pattern of the glaciers in HMA. The results show that glaciers with the lowest ablation index, lowest summer accumulation ratio which locates in the center of the HMA region, has the most balanced state (+0.05 m w.e/a). In contrast, the type with high ablation, median summer accumulation ratio, presents a severely negative mass balance (-0.47 m w.e/a). Under similar ablation intensity, the type of glaciers with higher summer precipitation is more negative. To assess the reliability of our method, we compare the index with the summer snow line, weather station records, and precipitation data. This work provides novel methods and knowledge to understand and project the glacier mass balance heterogeneity in HMA.

This project leverages the ESA and NRSCC opportunity to access satellite observations of Earth’s surface to assess precipitation and temperature biases in climate reanalyses, and, building on our previous project (ID 59199), to quantify blue runoff), green (evapotranspiration), and white (sublimation) water fluxes in high elevation catchments. We are applying the land surface models Tethys & Chloris (T&C), validated by independent observations, to deepen our understanding of the cryosphere and water cycle of key water towers in High Mountain Asia (HMA). The T&C model allows us to bridge the disciplinary gaps between snow, permafrost, and glaciers, which generate the runoff that ultimately feeds major rivers, and to consider downstream vegetation, which buffers, delays or amplifies that runoff.

Our main aim is to use Earth Observation data to constrain glacio- and eco-hydrological processes, in order to quantify the interplay of blue, green, and white water fluxes in glacierized catchments across High Mountain Asia. The focus of remote sensing data analysis is the observation and understanding of the relation between climate forcing through the surface energy balance of snow and ice and the cryosphere dynamics in terms of both mass and dynamics of snowpack and glaciers. The validated T&C model can assess how ecosystems and vegetation enhances or reduces glacier contributions to streamflow under climate change in HMA. The glacierized study sites span a range of climatic regimes, with several sites in the Pamir-Karakoram Anomaly domain. In addition to the remote sensing observations, field measurements allow independent evaluation of the model results, where available.
We determined seasonal and interannual variations in catchment snowline altitudes from multiple satellite imagers. We also determined seasonal and interannual changes in land surface temperature (Ren et al, 2024a) and snow/ice albedo. Ice thickness change data are being actively collected for Pamir locations.
A proof-of-concept of the method to estimate biases in climate reanalysis through ecohydrological modelling has been completed and tested for 4 sites with differing climates, using a Bayesian particle filter.
The hyper-resolution eco-hydrological modelling system has been improved, particularly as regards computationally efficiency. This has enabled long-term simulations with a dynamic glacier geometry module.
We developed an improved method to retrieve glacier surface velocity with an optimized image pair selection scheme and post-processing methods. We used the near-infrared images (10 m spatial resolution) acquired by the Sentinel-2 MSI L2A products and the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) method to retrieve the glacier velocity in Yanong glacier between 2020 and 2024.
We used daily China surface climate data and five machine learning methods to classify precipitation phases, and evaluated their performance in the temperature range (-7℃～10℃) that is difficult to accurately identify with traditional methods. By training and testing different models, their recognition accuracy was analyzed and compared with traditional empirical methods.
We determined daily 500 m river ice cover changes in six major high-latitude river basins in the Northern Hemisphere region from 2002 to 2022 and produced a daily 12.5 km river ice coverage dataset and a 20-year dataset on river ice phenology.